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Visual Analytics for Maritime Domain Awareness

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Visual Analytics for Maritime Domain Awareness Visual Analytics for Maritime Domain Awareness Valérie Lavigne, Denis Gouin Michael Davenport Defence R&D Canada - Valcartier Salience Analytics Inc. Québec (QC), Canada Vancouver (BC), Canada Abstract—Maintaining situation awareness in the maritime domain is a challenging mandate. Task analysis activities were III. VISUAL ANALYTICS conducted to identify where visual analytics science and Thomas and Cook define VA as “the science of analytical technology could improve maritime domain awareness and reasoning facilitated by interactive visual interfaces” [3]. The reduce information overload. Three promising opportunities goal of VA is to facilitate high-quality human judgement with a were identified: the visualization of normal maritime behaviour, limited investment of the analyst’s time. According to them, anomaly detection, and the collaborative analysis of a vessel of people use VA tools and techniques to: interest. In this paper, we describe the result of our user studies along with potential visual analytics solutions and features • “synthesize information and derive insight from considered for a maritime analytics prototype. massive, dynamic, ambiguous and often conflicting data; Keywords-visual analytics; maritime domain awareness; anomaly detection; situation analysis; collaborative work. • detect the expected and discover the unexpected; • provide timely, defensible, and understandable I. INTRODUCTION assessments; In this paper, we report on the results of work conducted by • Defence R&D Canada (DRDC) in the exploration of Visual communicate assessment effectively for action.” [3] Analytics (VA) solutions to real world maritime challenges and VA can help analyze entity behaviour, known patterns, and describe the design of a Maritime Analytics Prototype (MAP) the links between them. implementing VA features that will enable better situation understanding. First, we discuss Maritime Domain Awareness IV. MARITIME TASK ANALYSIS (MDA) and its challenges. Results from the task analysis studies identifying VA opportunities are presented, followed by A. Methodology related work. Then, we describe our proposed approach for the MAP along with some of the apps designs for various maritime Task analysis activities were conducted to identify the analysis tasks. We conclude with our plan for future work. opportunities and potential requirements for the application of VA in the RJOCs. II. MARITIME SURVEILLANCE Our methodology included the review of previous MDA is “the effective understanding of everything on, knowledge elicitation studies from related DRDC research under, related to, adjacent to or bordering a sea, ocean or other projects [4][5]. Then, we organized discussion sessions at the navigable waterway, including all maritime-related activities, RJOCs in Halifax and Esquimalt. We were also able to observe infrastructure, people, cargo, and vessels and other duty personnel in action in their work environment. The conveyances that could impact the security, safety, economy, domain knowledge was gathered through a mix of interviews, or environment.” [1] observations, and group discussions, then documented in a report [6]. In Canada, the Canadian Forces Regional Joint Operational Centers (RJOCs) in Esquimalt and Halifax maintain a 24/7 B. Opportunity Identification Results watch over Canada’s three oceans. The production and The latest requirement elicitation activity identified nine exploitation of the Recognized Maritime Picture (RMP) is an opportunities for applying VA to MDA. This paper summarizes essential part of their work. Watch personnel continually our current R&D activities focused on the first three: interpret the maritime situation with reference to space and time. They need to rapidly process a variety of sources of • Visualizing Normal Maritime Behaviour (VNMB) information in order to develop shared situational awareness. • This is a challenging task because of the large number of vessel Surveillance and Anomaly Detection (SAD) contacts and heterogeneous information sources that must be • Collaborative VA of a Vessel of Interest (CVAV) monitored. This leads to a significant information overload [2]. Fortunately, VA has emerged as a new and multidisciplinary We believe that these applications have the highest field that helps turn this information overload into an potential to gain improvement from VA solutions. However, opportunity. the other opportunities are well documented and could be the basis for future research projects [6]. V. RELATED WORK Visual exploration strategies can be used to extract patterns from past behaviour in a dynamic geo-spatial area of interest. A. Visual Analysis of Trajectories For example, the study of Somali pirate attacks [21] shows that Extensive work on the visual analysis of movement data led the attack pattern changed in response to the creation of the to the definition of aggregation methods suitable for movement Maritime Security Patrol Area. data and to VA ways to visualize and explore the statistical To the best of our knowledge, VA techniques have not been patterns from trajectories [7]. Interactive Kohonen maps were used in focused analysis of specific entities in the maritime also used for trajectories aggregation [8]. domain. General trends were often identified but the visual Clustering of trajectories helps analyze large group analysis of individuals has received much less attention. behaviours, but it does not easily allow visual identification of anomalous tracks. To this end, [9] produced ship density VI. MARITIME ANALYTICS PROTOTYPE landscapes in which ships that are off historic routes and The intent of the Maritime Analytics Prototype (MAP) is to regular traffic lanes visually stand out. Vessel movement improve the visualization of the recognized maritime picture patterns can also be characterized using hybrid fractal/velocity and help analysts better comprehend a situation and anticipate signatures [10]. As analysts learn to visually interpret these how it could develop. signatures, they can recognize anomalous activities. Innovative tools for geotemporal visualization of trajectories exist [11]. A. Mix of Original and Existing Concepts B. Maritime Anomaly Detection Our proposed Maritime Analytics Prototype (MAP) is a mixture of original innovative concepts and existing VA and With some exceptions (e.g. [9] and [10]), published work information visualization concepts that are not presently regarding visual detection of anomalies is mostly concerned applied to MDA. We may also integrate available academic with network security [12]. The geo-temporal characteristics of and commercial VA solutions such as GeoTime [11], JigSaw vessel trajectory data are very different from computer [22] and nSpace [23]. intrusions, so new strategies are required. Vessel track analysis, for example, is multidimensional and can be affected by factors The contribution of this work is twofold: such as: time of the day/week/year, seasons, meteorological • conditions, economic trends, and special events. Existing theoretical and applied VA approaches have been extended and assembled into a suite that is When considering maritime anomaly detection specifically, optimized for this application. we rapidly find that research has largely focused on developing • automated system that suggests potential anomalies to an New VA innovative concepts have been developed in operator for further investigation. Many of these maritime response to specific maritime domain requirements. anomaly detection systems rely on models of normal/abnormal vessel kinetic behaviour to detect anomalies. B. Apps Design Approach Because of their 24/7 operational context, the RJOCs have Self organizing maps were used and user can be involved in for many years upgraded their information technology through the anomaly detection process using interactive visualizations a series of incremental improvements, rather than through [12]. Reference [13] uses a Gaussian mixture model for system replacement cycles. Accordingly, the MAP will consist maritime anomaly detection while [14] uses a Bayesian of a set of services that could be added incrementally to a network. Spline-based trajectory clustering techniques were service-oriented architecture. The MAP has thus been designed proposed by [15] to represent normal vessel behaviour for as a set of compact software applications (apps), with the coastal surveillance. The use of a neurobiologically inspired following characteristics: algorithm for probabilistic associative learning of vessel motion was suggested by [16]. • Transient: each app can pop up when needed and then Rule-based expert systems rely on artificial intelligence hide away under an icon when not needed. rules derived from human expert knowledge acquisition • Self-contained: apps can be added as a service without activities. Such a system was built at DRDC [17], which we requiring changes to the operating system or to other will use as an input in our prototype. This approach was also applications. employed by [18]. • Single-purpose: users experience each app as doing C. Maritime Situation Analysis one specific job. Although the geospatial trajectory of a vessel is its most DRDC Valcartier and Salience Analytics Inc performed salient signature, maritime situation assessment requires the design studies activities that led to 24 apps concepts [24] analysis of more varied data such as port visit history, owner related to the three selected MDA opportunities. This paper relationships and suspected criminal activities.
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